Machine learning

Chemistry

Adversarial machine learning

Physics

Feature selection

Executable

Resilience (materials science)

Operating system

Computer science

Adversary

Gene

Malware

Adversarial system

Computer security

Robustness (evolution)

Thermodynamics

Artificial intelligence

Malware analysis

Biochemistry

Expert Systems with Applications

The use of machine learning algorithms for cyber security purposes gives rise to questions of adversarial resilience, namely: Can we quantify the effort required of an adversary to manipulate a system that is based on machine learning techniques? Can the adversarial resilience of such systems be formally modeled and evaluated? Can we quantify this resilience such that different systems can be compared using empiric metrics? Past works have demonstrated how an adversary can manipulate a system based on machine learning techniques by changing some of its inputs. However, comparatively little work has emphasized the creation of a formal method for measuring and comparing the adversarial resilience of different machine learning models to these changes. In this work we study the adversarial resilience of detection systems based on supervised machine learning models. We provide a formal definition for adversarial resilience while focusing on multisensory fusion systems. We define the model robustness (MRB) score, a...

Quantifying the resilience of machine learning classifiers used for cyber security